Organizations and enterprises all over the world use handwritten and printed documents, some more so than others. Particularly, organizations belonging to healthcare, banking, and insurance domains have important and confidential information in the form of numerous handwritten and printed documents. Further, information in these documents is required to be recognized and stored for different purposes by various enterprise applications and databases.
Conventionally, various systems and methods exist for digitizing printed and handwritten documents. For example, printed and handwritten documents are manually typed. However, manually typing the handwritten and printed documents is inefficient as productivity varies from person to person and depends on typing speed and errors involved in typing. Further, manual typing of the documents results in unnecessary exposure of confidential information.
To overcome the abovementioned disadvantages, systems and methods exist that automatically recognize characters in printed and handwritten documents. However, the abovementioned systems and methods also suffer from various disadvantages. These systems and methods are either incapable or inefficient in recognizing characters in case of cursive handwriting. Further, these systems and methods are unable to accurately segment text in the documents. Furthermore, the abovementioned systems and methods are unable to identify overlapping characters particularly in case of characters written in cursive handwriting.
In light of the abovementioned disadvantages, there is a need for a system and method for efficiently and accurately recognizing handwritten characters in one or more documents. Further, there is a need for a system and method that facilitates error correction using Natural Language Processing (NLP). Furthermore, there is a need for a context specific character recognition system and method. Also, there is a need for a system and method that facilitates real-time recognition of handwritten characters from the one or more documents belonging to different domains.